occu: Fit the MacKenzie Occupancy Model
In ianfiske/unmarked: Models for Data from Unmarked Animals

Description Usage Arguments Details Value Author(s) References Examples

This function estimates the standard occupancy model of MacKenzie et al (2002).

1 2	occu(formula, data, knownOcc=numeric(0), starts, method="BFGS", control=list(), se=TRUE)

`formula`	double right-hand side formula describing covariates of detection and occupancy in that order.
`data`	an unmarkedFrameOccu object (see unmarkedFrame)..
`knownOcc`	vector of sites that are known to be occupied.
`starts`	vector of parameter starting values.
`method`	Optimization method used by `optim`.
`control`	Other arguments passed to `optim`.
`se`	logical specifying whether or not to compute standard errors.

See unmarkedFrame for a description of how to supply data to the umf argument. occu fits the standard occupancy model based on zero-inflated binomial models (MacKenzie et al. 2006, Royle and Dorazio 2008). The occupancy state process (z_i) of site i is modeled as

z_i ~ Bernoulli(psi_i)

The observation process is modeled as

y_ij | z_i ~ Bernoulli(z_i * p_ij)

Covariates of psi_i and p_ij are modeled using the logit link according to the formula argument. The formula is a double right-hand sided formula like ~ detform ~ occform where detform is a formula for the detection process and occform is a formula for the partially observed occupancy state. See formula for details on constructing model formulae in R.

unmarkedFitOccu object describing the model fit.

Ian Fiske

MacKenzie, D. I., J. D. Nichols, G. B. Lachman, S. Droege, J. Andrew Royle, and C. A. Langtimm. Estimating Site Occupancy Rates When Detection Probabilities Are Less Than One. Ecology 83, no. 8 (2002): 2248-2255.

MacKenzie, D. I. et al. (2006) Occupancy Estimation and Modeling. Amsterdam: Academic Press. Royle, J. A. and R. Dorazio. (2008).

data(frogs)
pferUMF <- unmarkedFrameOccu(pfer.bin)
plot(pferUMF, panels=4)
# add some fake covariates for illustration
siteCovs(pferUMF) <- data.frame(sitevar1 = rnorm(numSites(pferUMF)))

# observation covariates are in site-major, observation-minor order
obsCovs(pferUMF) <- data.frame(obsvar1 = rnorm(numSites(pferUMF) * obsNum(pferUMF)))

(fm <- occu(~ obsvar1 ~ 1, pferUMF))

confint(fm, type='det', method = 'normal')
confint(fm, type='det', method = 'profile')

# estimate detection effect at obsvars=0.5
(lc <- linearComb(fm['det'],c(1,0.5)))

# transform this to probability (0 to 1) scale and get confidence limits
(btlc <- backTransform(lc))
confint(btlc, level = 0.9)